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27 november 2022 11:23:40

 
Remote Sensing, Vol. 14, Pages 6011: Assessment of Grassland Degradation on the Tibetan Plateau Based on Multi-Source Data (Remote Sensing)
 


Grassland is one of the most widely distributed ecosystems on the Tibetan Plateau (TP) accounting for about 60% of the total area. The grassland degradation has spread throughout the TP, and the scope and degree are increasing. The inconsistency of multi-source data poses a great challenge to accurately obtaining information about grassland degradation on the TP. This study used five land cover products and six vegetation indexes to analyze the spatial-temporal change in grassland area and quality at the pixel level across the TP from 2000 to 2020. Then, 279 observed grassland degradation points that were collected from 86 published papers were used to verify the grassland degradation information. The grassland fusion product demonstrated that the grassland area increased by 8.84% from 2000 to 2020, and the rate of grassland degradation exceeded the rate of grassland greening during 2010–2020. The superimposed six vegetation indexes showed that 25.88% of the grassland quality has been degraded on the TP from 2000 to 2020. In Changdu City, Ganzi Tibetan Autonomous Prefecture, Gannan Tibetan Autonomous Prefecture, Yushu Tibetan Autonomous Prefecture, Aba Tibetan and Qiang Autonomous Prefecture, Rikaze City, Shannan City and Nagqu City, the grassland quality degraded by more than in 20% and the degraded grassland area exceeded 2,000 km2. The observed grassland degradation points were mainly distributed in the northeastern and central parts of the TP. The consistency of six vegetation indexes with the observed grassland degradation points on the TP was 56.63%, with solar-induced chlorophyll fluorescence (SIF) being more effective than other vegetation indexes for monitoring grassland degradation on the TP. In general, the degradation of grassland on the TP has been a looming problem in recent decades.


 
94 viewsCategory: Geology, Physics
 
Remote Sensing, Vol. 14, Pages 6012: Terrestrial Water Storage Component Changes Derived from Multisource Data and Their Responses to ENSO in Nicaragua (Remote Sensing)
Remote Sensing, Vol. 14, Pages 6014: Cultivated Land Quality Evaluated Using the RNN Algorithm Based on Multisource Data (Remote Sensing)
 
 
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